📄 产生背景.html
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style="FONT-SIZE: 12pt; FONT-FAMILY: 'Times New Roman'; mso-bidi-font-size: 9.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-font-family: 宋体">2001</SPAN><SPAN
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体; mso-bidi-font-size: 9.0pt; mso-ascii-font-family: 'Times New Roman'; mso-hansi-font-family: 'Times New Roman'; mso-bidi-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA">年提出</SPAN><SPAN
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体; mso-bidi-font-size: 9.0pt; mso-ascii-font-family: 'Times New Roman'; mso-hansi-font-family: 'Times New Roman'; mso-bidi-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA">，该模型不同于产生式模型，它可以直接使用丰富的彼此重叠的观测序列的特征，而且不需要严格的前提假设。同时，它也不同于最大熵和最大熵马尔科夫模型，它不是对单个标记归一化后再进行全局搜索，而是在整个观测序列上求解一个最优的标记序列，避免了标记偏置问题。该模型在近几年来以其灵活性、包容性以及优异的处理结果已经被广泛地应用到了人工智能的很多领域，例如有句法分析</SPAN><SPAN
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